Ecology

DataWe used temperature data, rainfall data, and data on the incidence of malaria collected from 1901 to 2015 for 43 African countries to construct networks to determine the relationships between transmission of malaria and climate change elements, especially temperature and rainfall. Data resolution is given by the latitude and longitude of the capital city for every country in Africa. Temperature and rainfall data are provided in terms of monthly averages in the country wise. The nodes in the network represent the country, and the edges in the network represent the relationship between countries. We collected malaria data from Harvard Dataverse35 and the world malaria report from the WHO31. Data for temperature and rainfall were obtained from the Climate Change Knowledge Portal of the World Bank Group36.Network generation and analysisThe networks were constructed by using the threshold method where the network depends on the mean, standard deviation, and the real number ((n)) used to control the features of the network. Therefore, data for temperature, rainfall, and the incidence of malaria were divided into six groups mostly comprising ranges of 20 years (1900–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000) as well as the period from 2001 to 2015. The missing data in Malaria incidence data are filled by the average amount of malaria incidence collected per year.In Table S1, a malaria report from the World Health Organization shows that the rate of death is directly proportional to the incidence of malaria35. The death toll in Africa from malaria is about 98% of world deaths from malaria. Such deaths in African regions decrease thanks to efforts the WHO, governments, and the private sector have been conducting to prevent them. Weather and climate are among the factors that drive increases in malaria infections in different areas.We consider networks based on the threshold method (see the “Methods and Materials” section below). First, we fill the missing malaria incidence data, and we calculate normalized Pearson correlation coefficients of three-time series between two countries. Then, we obtain a correlation matrix for the countries. We estimate the average value of the correlation coefficients from the time intervals 1901–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000, and 2001–2015 for three time series: temperature, rainfall, and incidence of malaria. We summarize the averages and standard deviations of the correlation coefficients, as shown in Table S2. The mean values from the correlation in temperature are high, compared to those for rainfall and the incidence of malaria. The standard deviations in temperature and rainfall are large, but the standard deviation for the incidence of malaria is small.We chose an ad hoc threshold value of the correlation coefficients to generate sparse networks. The characteristic values for (n) of the threshold are given in Table S3. We consider three types of thresholds in order to observe changes in the networks according to the threshold.Let us define the normalized variance of each time series. We considered time series (T_{i} left( t right)), (M_{i} left( t right)), and (R_{i} left( t right)) in country (i) for temperature, the incidence of malaria, and rainfall, respectively. We define normalized variance as$$r_{ij} = frac{{x_{i} left( t right)x_{j} left( t right) – x_{i} left( t right)x_{j} left( t right)}}{{sigma_{i} sigma_{j} }}$$
(1)
where (x_{i} left( t right)) = (T_{i} left( t right)), (M_{i} left( t right)), (R_{i} left( t right)). We obtained a Pearson correlation matrix for each time series as follows:$$R_{S} = left[ {begin{array}{*{20}c} {r_{11} } & cdots & {r_{1N} } \ vdots & {r_{ij} } & vdots \ {r_{N1} } & cdots & {r_{NN} } \ end{array} } right]$$
(2)
where (S = T, M, R).We calculated the average value, (overline{r }), and the standard deviation, (sigma), for the correlation coefficients of the matrix. We applied the threshold method to generate a sparse network from the correlation matrix. Two countries are connected in the correlation network if and only if the value of the correlation coefficient is greater than, or equal to, the threshold value:$$r_{{ij}} = left{ {begin{array}{*{20}c} 1 & {{text{if}};r_{{ij}} ge bar{r}{text{ + n}}sigma } \ 0 & {{text{otherwise}}} \ end{array} } right.$$
(3)
where (r_{ij}) is the correlation coefficient between two countries, and (n) is an element of real numbers ((n in {mathbb{R}})). The value of (n) determines whether the network is sparsely or densely connected.We use Python programming language, packages, numpy for mathematical functions and random number generator, pandas for data analysis and manipulations, networkx for creation, manipulation, and studying the structure of the complex network, matplotlib for visualization and plotting graph and base map for map projection and visualization of geographic information. More

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Rabbits have had a disastrous impact on Australian agriculture and native plants.Credit: Bettman/Getty

A genomic analysis has helped to show that Australia’s invasive rabbit population probably originated from a shipment of two dozen wild English rabbits that arrived near Melbourne on Christmas Day, 1859. The study also finds that the herd’s wild ancestry probably gave it an advantage over previous arrivals.Rabbits have invaded most of the Australian continent and have had a disastrous impact on ecosystems, threatening some 300 species of plants and animals, and causing hundreds of millions of dollars’ worth of damage to the agriculture industry a year. “That single event triggered this enormous catastrophe, ecologically and economically, in Australia,” says Francis Jiggins, an evolutionary geneticist at the University of Cambridge, UK, and study co-author.Breeding like rabbitsHistorical records suggest that the first European rabbits (Oryctolagus cuniculus) in Australia arrived in Sydney in 1788, with the first colonizers. Ships bringing rabbits continued to dock along the coast for decades, but it wasn’t until the second half of the nineteenth century that the population expanded significantly, spreading across the country at a rate of 100 kilometres a year.Historical records also suggest that the rabbit expansion followed a shipment of animals that arrived for a certain Thomas Austin at Barwon Park, southwest of what is now Melbourne. His brother had trapped them around their family property in Baltonsborough in southwest England.Joel Alves, an evolutionary geneticist at the University of Oxford, UK, and his colleagues wanted to find out whether genomic data corroborated the records. They analysed genetic data from 179 wild rabbits caught across Australia and in New Zealand, France and the United Kingdom, as well as 8 domestic rabbits of different breeds.They found that most rabbits in mainland Australia were genetically similar, with mixed wild and domestic ancestry. Australian rabbits also shared more rare alleles with rabbits from southwest England than with those from elsewhere in the United Kingdom, suggesting they originated in Baltonsborough. Looking specifically at mitochondrial DNA, which is inherited from the mother, the researchers concluded that most mainland Australian rabbits descended from about five females, introduced from Europe.The researchers also found that the rabbits’ genetic diversity declined the further from Barwon Park the animals were caught, and that alleles that are rare or absent in wild rabbits increased. The researchers say these patterns are consistent with the idea that most rabbits across Australia originated from Barwon Park. The team report their findings in the Proceedings of the National Academy of Sciences on 22 August1.“This is a very exciting paper on a very important and well-studied topic,” says Martin Nuñez, who researches ecological invasions at the University of Houston in Texas. Using genetics to understand how unwanted animal invasions start can help to predict future invasions, he says.Perfect stormOverall, the team says that the rabbits’ wild ancestry was an important factor in triggering their invasion of the continent. “Wild rabbits are different,” says Alves. They exhibit traits such as fleeing stressful environments and burrow-digging, meaning that they were probably better at evading predators and surviving in difficult terrain than are domestic rabbits, he says. Historical records suggest that Austin requested wild rabbits, and that previous arrivals were largely domestic breeds.The expansion of Australian pastoral lands and widespread suppression of predators around that time would have also helped their expansion. “It was like a perfect storm,” says Alves. “You have the right rabbits in the right place at the right time, with the right changes in the environment.”“The genetic analyses appear very sound,” says rabbit geneticist Amy Iannella, a consultant based in Adelaide, Australia. She adds that although the country’s rabbit populations probably originated in Barwon Park, their rapid expansion might have been aided by people transporting the animals to other parts of the country, where they also began spreading. Rabbits are typically communal animals that rely on shelter for survival and juveniles rarely travel further than 1 kilometre, she says. “The idea of rabbits moving fast enough at the invasion front to colonize Australia so quickly from a single release, well that feels extreme to me, given what we know about rabbit ecology.” More

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Site descriptionThe study site (about 1 ha) is within a coastal dune ecosystem (35° 32′ 26.0″ N, 134° 12′ 27.5″ E) located at the Arid Land Research Center of Tottori University, Tottori, Japan. The mean annual temperature is 15.2 °C, and the mean total precipitation is 1931 mm, based on records collected from 1991 to 2020 at the Tottori observation station of the Japan Meteorological Agency. Dominant plant species around the measurement plot were Vitex rotundifolia and Artemisia capillaris. Carex kobomugi and Ischaemum anthephoroides were also scattered around the coastal side of the study site, and planted Pinus thunbergii trees cover the inland side.Experimental designIn May 2020, we established four measurement plots at the study site (Fig. 9). Plot 1 was a gap area surrounded by V. rotundifolia seedlings. Plot 2 consisted of clusters of V. rotundifolia seedlings and was adjacent to plot 1. Within plots 1 and 2, C. kobomugi and I. anthephoroides were also scattered. Plot 3 was in a mixed area of V. rotundifolia and A. capillaris; this plot was in the center of the study site. Plot 4 was located in front of P. thunbergii trees and was in the most inland area of the study site. On 10 June 2020, we set an environmental measurement system at the center of the study site adjacent to plot 3, and we then obtained continuous data for soil temperature and soil moisture. In each plot (main plot), we set 10 plastic (polypropylene) collars (n = 10) before the start of the Rs measurement. We measured Rs every 2 weeks from 15 June to 2 December 2020 in the main plots. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June and early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July (Fig. 9), and flux calculations for plot 1 were conducted after removing data from the invaded area measured on June 15.Figure 9Diagram and photos of measurement plots in the focal coastal dune ecosystem. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June to early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July.Full size imageEnvironmental measurement systemThe environmental measurement system was composed of a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA), battery (SC dry battery, Kind Techno Structure Co. Ltd, Saitama, Japan), solar panel (RNG-50D-SS, RENOGY International Inc., Ontario, CA, USA), charge controller (Solar Amp mini, CSA-MN05-8, DENRYO, Tokyo, Japan), thermocouples (E type), and soil moisture sensors (CS616, Campbell Scientific Inc.). The data logger, battery, and charge controller were kept in a plastic box to avoid exposure to rainfall and sand. Each end of the thermocouple was inserted into a copper tube (4-mm inner diameter, 5-cm length) and affixed with glue. To measure the reference soil temperature at different depths, copper tubes enclosing E-type thermocouples were buried horizontally in the sand at depths of 5, 10, 30, and 50 cm (n = 1 for each depth) at the center of plot 3 as reference soil temperature (the data was recorded every 30 min). In addition, we set stand-alone soil temperature sensors (Thermochron SL type, KN Laboratories, Inc. Osaka, Japan) at the center of plots 1 and 4 at depths of 5, 10, and 30 cm (n = 1 for each plot, each depth), and they recorded soil temperature data every 30 min. Reference soil temperature at the depth of 5, 10, and 30 cm was used for gap-filling for soil temperature measured by stand-alone sensors at each depth and plot. Soil moisture sensors were buried horizontally in the sand at a depth of 30 cm in the center of plots 1, 3, and 4 (n = 1 for each plot) and recorded data every 30 min. Raw values of soil moisture sensors were converted to volumetric soil moisture (%) using a calibration line from 0 to 15% measured in the laboratory using dune sand and three sensors (CS616) referring to the procedure of Bongiovanni et al.53. Data for precipitation at the local meteorological observatory in Tottori was downloaded from the home page of the Japan Meteorological Agency (https://www.data.jma.go.jp/gmd/risk/obsdl/index.php).
R
s measurement in the main plotsPolypropylene collars (30-cm inner diameter, 5-cm depth, n = 10) were set in each measurement plot in late May 2020. The first Rs measurement was conducted on 15 June 2020. However, V. rotundifolia and C. kobomugi then invaded about half of the gap area of plot 1, so on 1 July we set 5 new polypropylene collars for plot 1 to replace the 5 invaded measurement points (Fig. 9). The second Rs measurement was conducted on 2 July, and all polypropylene collars then remained in the same position until the end of the measurement period.Rs was measured using an automated closed dynamic chamber system54 composed of two cylindrical aluminum chambers (30 cm diameter, 30 cm height) equipped with thermistor temperature sensors (44006, Omega Engineering, Stanford, CA, USA) for measuring air temperature inside the chamber during Rs measurement. Those chambers were connected to a control box equipped with a pump, data logger (CR1000, Campbell Scientific Inc.), CO2 analyzer (Gascard NG infrared gas sensor, Edinburgh Sensors, Lancashire, UK), and thermometer (MHP, Omega Engineering). The composition of the control box is basically the same as used in previous studies54,55. The measurement period for each point was 3 min, and the CO2 concentration and air temperature inside the chamber were recorded every 5 s. During the measurement, another chamber was set on the next polypropylene collar with the lid opened, and the next measurement was started at that moment of finishing the previous measurement by automatically closing the chamber lid on the next polypropylene collar in the same plot. Soil temperature at a depth of 0–5 cm was recorded simultaneously by inserting the rod of the thermometer vertically into the soil surface near the polypropylene collar (about 1–2 m from the collar).Rs was calculated by using the following equation:$$R_{{text{s}}} = frac{{PV}}{{RS(T_{{{text{air}}}} + 273.15)}}frac{{partial C}}{{partial t}},$$
(1)
where P is the air pressure (Pa), V is the effective chamber volume (m3), R is the ideal gas constant (8.314 Pa m3 K−1 mol−1), S is the soil surface area (m2), Tair is the air temperature inside the chamber (°C). ∂C/∂t is the rate of change of the CO2 mole fraction (μmol mol−1 s−1), which was calculated using least-squares regression of the CO2 changes inside the chamber12. For the flux calculation, we removed data for the first 35 s (dead band) of each measurement as an outlier.Trench treatment and soil CO2 efflux (F
c) measurement in subplotsIn November 2020, we conducted root-cut treatment (trench treatment) in subplots using polyvinyl chloride (PVC) tubes to estimate the contribution of Ra to Rs in the soil layer above 50 cm in each plot (Ra_50/Rs). Small PVC collars (10.7 cm inner diameter, 5 cm depth, n = 10 for each plot), with the upper ends about 1–2 cm above the soil surface, were set in subplots adjacent to the main plots on 23 October 2020. Rs was measured in subplots using two cylindrical mini PVC chambers (11.8 cm inner diameter at the bottom, 30 cm height, equipped with the same thermistors as cylindrical aluminum chambers for air temperature measurement) connected to the same control box as used for Rs measurement in the main plots. The measurement period was 3 min, and the measurement procedure and the flux calculation were the same as the main plot. Rs was first measured in subplots on 3 November to examine the spatial variation of Rs before trench treatment. Using the data, we selected subplots to conduct trench treatment and control plots for comparison, while aiming to achieve a minimal difference in the average Rs between control and pre-trenched plots. On 4 November, we inserted PVC tubes (10.7 cm inner diameter, 50 cm length) into about half (n = 3–5) of the subplots (the same position as PVC collars were set on 23 October) by using a hammer and aluminum lid until the upper end of each PVC tube was 1–2 cm above the soil surface to exclude roots to a depth of about 50 cm. On 19 November, after 15 days of trench treatment, respiration was measured in the same subplots.The Ra_50/Rs was calculated as follows:$$R_{{{text{a}}_{5}0}} /R_{{text{s}}} = (F_{{{text{c}}_{text{control}}}} -F_{{{text{c}}_{text{trenched}}}}) /F_{{{text{c}}_{text{control}}}} ,$$
(2)
where Fc_trenched and Fc_control (= Rs) are the Fc values in trenched and control plots on 19 November, respectively.In late December 2020, all the belowground plant biomass (BPB) in subplots (control and trenched plots) to a depth of 50 cm was collected for biomass analysis, about 2 months after trench treatment. In the laboratory, all the collected plant materials were washed and oven-dried for 72 h at 70 °C, and then the dry weight of the BPB samples was measured.Biomass measurementWe conducted BPB analysis from 18 May to 8 June 2021 in each plot (n = 1). At that time, 100 cm × 100 cm sampling plots near the CO2 measurement plots (100 cm × 100 cm for plots 2–4 and 50 cm × 50 cm in plot 1 because of the narrow gap area) were dug to a depth of 100–220 cm, according to the root distribution in each plot, and all plant materials were collected by passing the soil through 5- to 7-mm sieves. Once we reached a depth where no roots were visible, no more digging was conducted. In plots 2 and 3, stolons of V. rotundifolia were difficult to distinguish from roots if underground. Therefore, we defined plant material as BPB if it was underground. In the laboratory, all of the collected plant materials were washed and air-dried at room temperature for 0–6 days depending on the biomass. After that, samples were oven-dried for 15–25 h at 70–80 °C, and the dry weight of those samples was then measured.Soil organic carbon and nitrogenOn 21 October 2020, soil pits were dug to a depth of 50 cm near each plot (n = 3), and soil core samples were collected. Cylindrical stainless core samplers (5 cm diameter, 5 cm height, 100 cc) were horizontally inserted into the soil pit at depths of 0–5, 5–10, 10–20, and 20–30 cm. In the laboratory, soil core samples were weighed and oven-dried at 105 °C for 48 h, and the dry weight was measured. Oven-dried soil samples were sieved with a 2-mm-pore stainless wire mesh screen, and visible fungal mycelia in soil samples from plot 4 were removed as well as possible. Sieved samples were ground with an agate mortar. Samples (fine powder) were oven-dried for 24 h at 105 °C and weighed before SOC and nitrogen analysis. About 1.5 g of powdered samples were used for the analysis. Organic carbon content (combustion at 400 °C) and total nitrogen in samples were analyzed using a Soli TOC cube (Elementar Analysensysteme GmbH, Langenselbold, Germany) by the combustion method.Microbial abundanceOn 21 October 2020, soil samples for microbial analysis were collected at the same time as soil core sampling for SOC and nitrogen analysis. Soil samples were collected at depths of 0–10, 10–20, and 20–30 cm using a stainless spatula and placed individually in a polyethylene bag. The bags were kept in a cooler box with ice in the field and then placed in a freezer (− 30 °C) in the laboratory soon after sampling.DNA was extracted from 0.5 g of the fresh soils using NucleoSpin Soil (Takara Bio, Inc., Shiga, Japan) according to the manufacturer’s instructions (SL1 buffer), and the extracts were stored at − 20 °C until further analysis. Bacterial and archaeal 16S rRNA and fungal internal transcribed spacer (ITS) gene were targeted to investigate the microbial abundance. Bacterial and archaeal 16S rRNA (V4 region) and fungal ITS were determined using the universal primer sets 515F/806R and ITS1F_KYO2/ITS2_KYO2, respectively56,57.For qPCR, samples were prepared with 10 μL of the KAPA SYBR Fast qPCR kit (Kapa Biosystems, Wilmington, MA, USA), 0.8 μL of forward primer, 0.8 μL of reverse primer, and 3 μL of 1–50 × diluted soil DNA. Nuclease-free water was added to make up to a final volume of 20 μL. Cycling conditions of 16S rRNA were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min. Cycling conditions of ITS were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 55 °C for 1 min, and 72 °C for 1 min. A melting curve analysis was performed in a final cycle of 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 15 s. High amplification efficiencies of 99% for bacterial and archaeal 16S rRNA genes and 101% for the fungal ITS were obtained based on the standard curves.Data analysisTo examine the environmental response (soil temperature and soil moisture) of Rs, nonlinear and quadratic regression models were applied. We conducted F-tests by comparing the regression model to a constant model whose value is the mean of the observations (significance set at p  More